Data Summary
income <- read.csv("https://ecoleman451.github.io/ecoleman/w6/income_per_person.csv")
life <- read.csv("https://ecoleman451.github.io/ecoleman/w6/life_expectancy_years.csv")
# Reshape data set such that there are only three columns (Geo, Year, & Income)
new_income <- pivot_longer(income, cols = -geo, names_to = "year", values_to = "income")
new_life <- pivot_longer(life, cols = -geo, names_to = "year", values_to = "life.expectancy")
## Create new data set
LifeExpIncom <- merge(new_life, new_income, by = c("geo", "year"))
## Read in More Data
country <- read.csv("https://ecoleman451.github.io/ecoleman/w6/countries_total.csv")
pop <- read.csv("https://ecoleman451.github.io/ecoleman/w6/population_total.csv")
new_pop <- pivot_longer(pop, cols = -geo, names_to = "year", values_to = "population")
## Merge LifeExpIncom with Country
merged <- merge(LifeExpIncom, country, by.x = "geo", by.y = "name", all.x = TRUE)
## Merge Population with Merged Data
fin_data <- merge(new_pop, merged, by = c("geo", "year"), all.x = TRUE)
## Get Data for Year 2015
final_data <- subset(fin_data, year =="X2015")
summary(final_data)
geo year population life.expectancy
Length:195 Length:195 Min. :8.030e+02 Min. :49.60
Class :character Class :character 1st Qu.:1.955e+06 1st Qu.:66.05
Mode :character Mode :character Median :8.320e+06 Median :73.30
Mean :3.768e+07 Mean :71.93
3rd Qu.:2.725e+07 3rd Qu.:77.50
Max. :1.400e+09 Max. :83.80
NA's :8
income alpha.2 alpha.3 country.code
Min. : 623 Length:195 Length:195 Min. : 4.0
1st Qu.: 3270 Class :character Class :character 1st Qu.:209.0
Median : 10800 Mode :character Mode :character Median :418.0
Mean : 17179 Mean :424.9
3rd Qu.: 24100 3rd Qu.:642.8
Max. :120000 Max. :894.0
NA's :8 NA's :21
iso_3166.2 region sub.region intermediate.region
Length:195 Length:195 Length:195 Length:195
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
region.code sub.region.code intermediate.region.code
Min. : 2.00 Min. : 15.0 Min. : 5.00
1st Qu.: 2.00 1st Qu.: 54.0 1st Qu.:11.00
Median : 19.00 Median :154.0 Median :14.00
Mean : 71.74 Mean :177.9 Mean :14.89
3rd Qu.:142.00 3rd Qu.:202.0 3rd Qu.:17.00
Max. :150.00 Max. :419.0 Max. :29.00
NA's :21 NA's :21 NA's :119
Plotly
scatter_plot <- plot_ly(
data = final_data,
x = ~income,
y = ~life.expectancy,
size = ~population,
color = ~geo,
text = ~paste("Country: ", geo, "<br>Population: ", population),
type = "scatter",
mode = "markers",
marker = list(
opacity = 0.6, # Transparency level
sizemode = "diameter", # Set the size mode to diameter
sizeref = 0.1, # Adjust the size reference for better visibility
line = list(
color = "black", # Boundary color for points
width = 1 # Boundary width
)
)
)
layout <- list(
title = "Association Between Life Expectancy and Income (Year 2015)",
xaxis = list(title = "Income"),
yaxis = list(title = "Life Expectancy"),
showlegend = FALSE # Hide legend for individual countries
)
# Combine the plot and layout
scatter_plot <- layout(scatter_plot, layout)
# Display the interactive scatter plot
scatter_plot
---
title: "Plotly"
author: "Edward Coleman"
date: "West Chester University"
output:
  html_document: 
    code_folding: hide
    code_download: yes
    smooth_scroll: yes
    theme: lumen
editor_options:
  chunk_output_type: inline
---

<style type="text/css">

div#TOC li {
    list-style:none;
    background-color:lightgray;
    background-image:none;
    background-repeat:none;
    background-position:0;
    font-family: Arial, Helvetica, sans-serif;
    color: #780c0c;
}

h1.title {
  font-size: 24px;
  color: DarkRed;
  text-align: center;
  font-family: Arial, Helvetica, sans-serif;
  font-variant-caps: normal;
}
h4.author { 
    font-size: 18px;
  font-family: "Times New Roman", Times, serif;
  color: DarkRed;
  text-align: center;
}
h4.date { 
  font-size: 18px;
  font-family: "Times New Roman", Times, serif;
  color: DarkBlue;
  text-align: center;
}
h1 { 
    font-size: 22px;
    font-family: "Times New Roman", Times, serif;
    color: darkred;
    font-weight: bold;
    text-align: center;
}
h2 { 
    font-size: 18px;
    font-family: "Times New Roman", Times, serif;
    color: navy;
    text-align: left;
}

h3 { 
    font-size: 18px;
    font-family: "Times New Roman", Times, serif;
    color: navy;
    font-weight: bold;
    text-align: left;
}

h4 {
    font-size: 18px;
    font-family: "Times New Roman", Times, serif;
    color: darkred;
    text-align: left;
}


/* Tab features */
.nav>li>a {
    position: relative;
    display: block;
    padding: 2px 15px;
    color: #990000;
}
.nav-pills>li.active>a, .nav-pills>li.active>a:hover, .nav-pills>li.active>a:focus {
    color: #ffffff;
    background-color: #990000;
}
/*
nav-pills>li:nth-child(2) {
    background: green;
 }
 */
</style>

```{r setup, include=FALSE}
options(repos = list(CRAN="http://cran.rstudio.com/"))
if (!require("tidyverse")) {
   install.packages("tidyverse")
   library(tidyverse)
}
if (!require("knitr")) {
   install.packages("knitr")
   library(knitr)
}
if (!require("cowplot")) {
   install.packages("cowplot")
   library(cowplot)
}
if (!require("latex2exp")) {
   install.packages("latex2exp")
   library(latex2exp)
}
if (!require("plotly")) {
   install.packages("plotly")
   library(plotly)
}
if (!require("gapminder")) {
   install.packages("gapminder")
   library(gapminder)
}
if (!require("png")) {
    install.packages("png")    
    library("png")
}
if (!require("RCurl")) {
    install.packages("RCurl")    
    library("RCurl")
}
if (!require("colourpicker")) {
    install.packages("colourpicker")              
    library("colourpicker")
}
if (!require("gganimate")) {
    install.packages("gganimate")              
    library("gganimate")
}
if (!require("gifski")) {
    install.packages("gifski")              
    library("gifski")
}
if (!require("magick")) {
    install.packages("magick")              
    library("magick")
}
if (!require("grDevices")) {
    install.packages("grDevices")              
    library("grDevices")
}
if (!require("jpeg")) {
    install.packages("jpeg")              
    library("jpeg")
}
if (!require("ggridges")) {
    install.packages("ggridges")              
    library("ggridges")
}
if (!require("plyr")) {
    install.packages("plyr")              
    library("plyr")
}
if (!require("ggiraph")) {
    install.packages("ggiraph")              
    library("ggiraph")
}
if (!require("highcharter")) {
    install.packages("highcharter")              
    library("highcharter")
}
if (!require("forecast")) {
    install.packages("forecast")              
    library("forecast")
}
## 
knitr::opts_chunk$set(echo = TRUE,       
                      warning = FALSE,   
                      result = TRUE,   
                      message = FALSE,
                      comment = NA)
```

# Data Summary
```{r}
income <- read.csv("https://ecoleman451.github.io/ecoleman/w6/income_per_person.csv")

life <- read.csv("https://ecoleman451.github.io/ecoleman/w6/life_expectancy_years.csv")

# Reshape data set such that there are only three columns (Geo, Year, & Income)
new_income <- pivot_longer(income, cols = -geo, names_to = "year", values_to = "income")

new_life <- pivot_longer(life, cols = -geo, names_to = "year", values_to = "life.expectancy")

## Create new data set
LifeExpIncom <- merge(new_life, new_income, by = c("geo", "year"))

## Read in More Data
country <- read.csv("https://ecoleman451.github.io/ecoleman/w6/countries_total.csv")

pop <- read.csv("https://ecoleman451.github.io/ecoleman/w6/population_total.csv")

new_pop <- pivot_longer(pop, cols = -geo, names_to = "year", values_to = "population")

## Merge LifeExpIncom with Country
merged <- merge(LifeExpIncom, country, by.x = "geo", by.y = "name", all.x = TRUE)

## Merge Population with Merged Data
fin_data <- merge(new_pop, merged, by = c("geo", "year"), all.x = TRUE)

## Get Data for Year 2015
final_data <- subset(fin_data, year =="X2015")
summary(final_data)
```


# Plotly
```{r}
scatter_plot <- plot_ly(
  data = final_data,
  x = ~income,
  y = ~life.expectancy,
  size = ~population,
  color = ~geo,
  text = ~paste("Country: ", geo, "<br>Population: ", population),
  type = "scatter",
  mode = "markers",
  marker = list(
    opacity = 0.6,  # Transparency level
    sizemode = "diameter",  # Set the size mode to diameter
    sizeref = 0.1,  # Adjust the size reference for better visibility
    line = list(
      color = "black",  # Boundary color for points
      width = 1  # Boundary width
    )
  )
)
layout <- list(
  title = "Association Between Life Expectancy and Income (Year 2015)",
  xaxis = list(title = "Income"),
  yaxis = list(title = "Life Expectancy"),
  showlegend = FALSE  # Hide legend for individual countries
)

# Combine the plot and layout
scatter_plot <- layout(scatter_plot, layout)

# Display the interactive scatter plot
scatter_plot
```
